It is natural to transfer mainstream 3D object detectors such as PointNet++ [1]} and DGCNN [2]} to visual quality representation of point clouds. However, different from the point clouds used for classification and segmentation, the high-quality point clouds usually contain millions of dense points, which makes it difficult to extract features directly from the source points unless utilizing down-sampling. However, the common sampling methods are aimed at preserving semantic information whereas inevitably damaging the geometry patterns that are crucial for quality assessment. To avoid the geometry error caused by the down-sampling and preserve the smoothness and roughness of local patterns, we propose to split the point cloud into several local sub-models for geometric structure feature representation. Specifically, given a normalized point cloud \(\mathbf {\hat{P}}\) , we employ the farthest point sampling (FPS) to obtain \(N_{\delta }\)  anchor points \(\lbrace \delta _{m}\rbrace _{m=1}^{N_{\delta }}\) . For each anchor point, we utilize K nearest neighbor (KNN) algorithm to find \(N_{s}\)  neighboring points around the anchor point and form such points into a sub-model:
<FIGURE>\({\rm S} = \lbrace \mathop {\rm {KNN}}\limits _{k=N_{s}}(\delta _{m})\rbrace _{m=1}^{N_{\delta }},\) 
